{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNnHgC0INV5gC+75pBfvvrl",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sugarforever/LangChain-Tutorials/blob/main/LangChain_ChatGithub.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2o-rPJYwWLh8",
        "outputId": "4952e7d1-8153-44a8-a0fc-00202ea37625"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: deeplake\n",
            "  Building wheel for deeplake (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for deeplake: filename=deeplake-3.3.2-py3-none-any.whl size=571661 sha256=4239e6cf3cf256a64a9b59839fab4de33f22da94b65a711aa71f4462ff46b0fb\n",
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            "Successfully built deeplake\n",
            "Installing collected packages: pyjwt, ppft, pox, numcodecs, mypy-extensions, multidict, marshmallow, jmespath, frozenlist, dill, async-timeout, aioitertools, yarl, typing-inspect, tiktoken, openapi-schema-pydantic, multiprocess, marshmallow-enum, humbug, botocore, aiosignal, s3transfer, pathos, dataclasses-json, aiohttp, openai, langchain, boto3, aiobotocore, aioboto3, deeplake\n",
            "Successfully installed aioboto3-11.1.0 aiobotocore-2.5.0 aiohttp-3.8.4 aioitertools-0.11.0 aiosignal-1.3.1 async-timeout-4.0.2 boto3-1.26.76 botocore-1.29.76 dataclasses-json-0.5.7 deeplake-3.3.2 dill-0.3.6 frozenlist-1.3.3 humbug-0.3.1 jmespath-1.0.1 langchain-0.0.152 marshmallow-3.19.0 marshmallow-enum-1.5.1 multidict-6.0.4 multiprocess-0.70.14 mypy-extensions-1.0.0 numcodecs-0.11.0 openai-0.27.5 openapi-schema-pydantic-1.2.4 pathos-0.3.0 pox-0.3.2 ppft-1.7.6.6 pyjwt-2.6.0 s3transfer-0.6.0 tiktoken-0.3.3 typing-inspect-0.8.0 yarl-1.9.2\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade langchain deeplake openai tiktoken"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "from langchain.embeddings.openai import OpenAIEmbeddings\n",
        "from langchain.vectorstores import DeepLake\n",
        "\n",
        "os.environ['OPENAI_API_KEY'] = ''\n",
        "os.environ['ACTIVELOOP_TOKEN'] = ''\n"
      ],
      "metadata": {
        "id": "nTRCqa1d0zSZ"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings = OpenAIEmbeddings(disallowed_special=())"
      ],
      "metadata": {
        "id": "LbSQL7qq0_la"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/chroma-core/chroma.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ciO_aYtP1EA3",
        "outputId": "3e510ab6-29bf-4cf4-aeb2-b49455dda961"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'chroma'...\n",
            "remote: Enumerating objects: 11173, done.\u001b[K\n",
            "remote: Counting objects: 100% (1443/1443), done.\u001b[K\n",
            "remote: Compressing objects: 100% (508/508), done.\u001b[K\n",
            "remote: Total 11173 (delta 1082), reused 1148 (delta 907), pack-reused 9730\u001b[K\n",
            "Receiving objects: 100% (11173/11173), 171.85 MiB | 31.83 MiB/s, done.\n",
            "Resolving deltas: 100% (7154/7154), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from langchain.document_loaders import TextLoader\n",
        "\n",
        "root_dir = './chroma'\n",
        "docs = []\n",
        "for dirpath, dirnames, filenames in os.walk(root_dir):\n",
        "    for file in filenames:\n",
        "        try: \n",
        "            loader = TextLoader(os.path.join(dirpath, file), encoding='utf-8')\n",
        "            docs.extend(loader.load_and_split())\n",
        "        except Exception as e: \n",
        "            pass"
      ],
      "metadata": {
        "id": "Cl3XkDfJ1eBl"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(docs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPGsYWURF9i9",
        "outputId": "bae55e03-e096-4677-bea4-82ec227be093"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "411"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.text_splitter import CharacterTextSplitter\n",
        "\n",
        "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
        "texts = text_splitter.split_documents(docs)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hTMD_m6S1jpm",
        "outputId": "a0e1ab29-2fca-4c8a-e528-887b19441d02"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:langchain.text_splitter:Created a chunk of size 1030, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1103, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1540, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1020, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1030, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1103, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1036, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1406, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 2415, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1293, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1090, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1023, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1116, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1106, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1299, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1097, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1381, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1341, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1493, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 2676, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1129, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 2435, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1572, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 1688, which is longer than the specified 1000\n",
            "WARNING:langchain.text_splitter:Created a chunk of size 2528, which is longer than the specified 1000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(texts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "011k3P5oGCEI",
        "outputId": "74b9fe1f-a1b7-407e-a95a-467c6de2a493"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "965"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "username = \"wyang14\"\n",
        "db = DeepLake(dataset_path=f\"hub://{username}/chroma_source\", embedding_function=embeddings, public=True)\n",
        "db.add_documents(texts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wVcjOx-N1oLu",
        "outputId": "2ea6a414-1e33-427c-87e2-c900b67ad07d"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your Deep Lake dataset has been successfully created!\n",
            "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/wyang14/chroma_source\n",
            "hub://wyang14/chroma_source loaded successfully.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evaluating ingest: 100%|██████████| 1/1 [00:18<00:00\n",
            "\\"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset(path='hub://wyang14/chroma_source', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
            "\n",
            "  tensor     htype      shape      dtype  compression\n",
            "  -------   -------    -------    -------  ------- \n",
            " embedding  generic  (965, 1536)  float32   None   \n",
            "    ids      text     (965, 1)      str     None   \n",
            " metadata    json     (965, 1)      str     None   \n",
            "   text      text     (965, 1)      str     None   \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r \r"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              " 'a981277e-e618-11ed-80c1-0242ac1c000c',\n",
              " 'a98127d8-e618-11ed-80c1-0242ac1c000c',\n",
              " 'a981283c-e618-11ed-80c1-0242ac1c000c',\n",
              " 'a98128a0-e618-11ed-80c1-0242ac1c000c',\n",
              " 'a9812922-e618-11ed-80c1-0242ac1c000c',\n",
              " 'a9812986-e618-11ed-80c1-0242ac1c000c',\n",
              " 'a98129e0-e618-11ed-80c1-0242ac1c000c',\n",
              " 'a9812a44-e618-11ed-80c1-0242ac1c000c',\n",
              " 'a9812a9e-e618-11ed-80c1-0242ac1c000c']"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "db = DeepLake(dataset_path=\"hub://wyang14/chroma_source\", read_only=True, embedding_function=embeddings)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tMhy-xdn1vMw",
        "outputId": "ee0af4f0-c411-4f18-fd4d-349ac6374fa2"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "-"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/wyang14/chroma_source\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "|"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hub://wyang14/chroma_source loaded successfully.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r \r\r \rWARNING:langchain.vectorstores.deeplake:Deep Lake Dataset in hub://wyang14/chroma_source already exists, loading from the storage\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset(path='hub://wyang14/chroma_source', read_only=True, tensors=['embedding', 'ids', 'metadata', 'text'])\n",
            "\n",
            "  tensor     htype      shape      dtype  compression\n",
            "  -------   -------    -------    -------  ------- \n",
            " embedding  generic  (965, 1536)  float32   None   \n",
            "    ids      text     (965, 1)      str     None   \n",
            " metadata    json     (965, 1)      str     None   \n",
            "   text      text     (965, 1)      str     None   \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retriever = db.as_retriever()\n",
        "retriever.search_kwargs['distance_metric'] = 'cos'\n",
        "retriever.search_kwargs['fetch_k'] = 100\n",
        "retriever.search_kwargs['maximal_marginal_relevance'] = True\n",
        "retriever.search_kwargs['k'] = 10\n"
      ],
      "metadata": {
        "id": "aLlxAZ4S10GE"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chat_models import ChatOpenAI\n",
        "from langchain.chains import ConversationalRetrievalChain\n",
        "\n",
        "model = ChatOpenAI(model='gpt-3.5-turbo')\n",
        "qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever)"
      ],
      "metadata": {
        "id": "zsKsATuG190J"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "questions = [\n",
        "    \"What does Chroma do?\",\n",
        "    \"How to use Chroma?\"\n",
        "] \n",
        "chat_history = []\n",
        "\n",
        "for question in questions:  \n",
        "    result = qa({\"question\": question, \"chat_history\": chat_history})\n",
        "    chat_history.append((question, result['answer']))\n",
        "    print(f\"Question:\\n {question} \\n\")\n",
        "    print(f\"Answer:\\n {result['answer']} \\n\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8xhQH_VG2BZf",
        "outputId": "992d296f-a3eb-4041-e7de-a56e392be261"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question:\n",
            " What does Chroma do? \n",
            "\n",
            "Answer:\n",
            " Chroma is an open-source embedding database that makes it easy to build LLM apps by making knowledge, facts, and skills pluggable for LLMs. It provides a JS/TS interface to talk to a backend Chroma DB over REST. Chroma stores embeddings and allows you to search by nearest neighbors rather than by substrings like a traditional database. By default, Chroma uses Sentence Transformers to embed for you but you can also use OpenAI embeddings, Cohere (multilingual) embeddings or your own. \n",
            "\n",
            "\n",
            "Question:\n",
            " How to use Chroma? \n",
            "\n",
            "Answer:\n",
            " Sure, here is a brief summary of how to get started with Chroma:\n",
            "\n",
            "1. Install Chroma: Run `pip install chromadb` for the Python client or `npm install chromadb` for the JavaScript client. \n",
            "2. Start Chroma: Follow the [Usage Guide](https://docs.trychroma.com/usage-guide) to quickly stand Chroma up.\n",
            "3. Import Chroma: In Python, `import chromadb`. In JavaScript, `import { ChromaClient } from 'chromadb'`. \n",
            "4. Use Chroma: You can create collections, add documents, and query them using the `chroma` object. See the [Small Example](https://github.com/chroma-core/chroma#small-example) from the Chroma Github repo for an idea of how to use Chroma. \n",
            "\n",
            "I hope that helps! Let me know if you have any more questions. \n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def ask(question, chat_history):\n",
        "  response = qa({\"question\": question, \"chat_history\": chat_history})\n",
        "  print(f\"Question:\\n {question}\\n\")\n",
        "  print(f\"Answer:\\n {response['answer']}\\n\")"
      ],
      "metadata": {
        "id": "q_DYKLNmBrPK"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ask(\"What's the main programming language used in Chroma?\", chat_history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RQ6d_NJx4bsD",
        "outputId": "a7118a55-a206-4117-9323-b483a5d1f9b8"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question:\n",
            " What's the main programming language used in Chroma?\n",
            "\n",
            "Answer:\n",
            " Chroma uses two primary programming languages - Python and JavaScript/TypeScript. The Chroma backend is built in Python, while the frontend and client libraries use JavaScript/TypeScript.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ask('Summarize the storage part of Chroma', chat_history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vMNXbAS64xiy",
        "outputId": "a827dbf9-ee9c-48d4-8a7f-5ccf78ad03c6"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question:\n",
            " Summarize the storage part of Chroma\n",
            "\n",
            "Answer:\n",
            " Chroma is an open-source embedding database that stores embeddings and allows you to search by nearest neighbors rather than by substrings like a traditional database. By default, Chroma uses Sentence Transformers to embed for you but you can also use OpenAI embeddings, Cohere (multilingual) embeddings, or your own. Chroma can be run in-memory, with persistence, or as a client-server with a small frontend client. Chroma can be used with Python or JavaScript.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ask('Tell me more about Sentence Transformers', chat_history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lpcokdnz4_7R",
        "outputId": "027ee649-b309-4b6c-aea7-541ccd92153c"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question:\n",
            " Tell me more about Sentence Transformers\n",
            "\n",
            "Answer:\n",
            " Sentence Transformers are a type of embedding function used to convert data like text, images, and audio into lists of numbers that can be understood by machine learning models. Chroma uses Sentence Transformers by default to embed data for users, but it also allows you to use other types of embeddings like OpenAI or Cohere embeddings. The package code for the Sentence Transformer embedding function can be found in the context above.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ask('Show me some example code on how to use Chroma to store embeddings', chat_history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xrLDPlJB9LxB",
        "outputId": "3b22cd38-f510-4591-a884-deec3c3b07ea"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question:\n",
            " Show me some example code on how to use Chroma to store embeddings\n",
            "\n",
            "Answer:\n",
            " Yes, sure! Here's an example code for storing embeddings using Chroma:\n",
            "\n",
            "```\n",
            "import chromadb\n",
            "\n",
            "client = chromadb.Client()\n",
            "client.reset()\n",
            "\n",
            "# Create a collection\n",
            "collection = client.create_collection(name=\"test_collection\")\n",
            "\n",
            "# Add documents with their embeddings\n",
            "ids = [\"doc1\", \"doc2\"]\n",
            "embeddings = [\n",
            "    [1.1, 2.3, 3.2],\n",
            "    [4.5, 5.5, 6.6]\n",
            "]\n",
            "\n",
            "collection.add(ids, embeddings)\n",
            "\n",
            "# Get embeddings of a document\n",
            "doc_id = \"doc1\"\n",
            "results = collection.get(ids=[doc_id], include=[chromadb.GetEmbeddingIncludeEnum.Embeddings])\n",
            "\n",
            "print(results.embeddings[0])  # prints [1.1, 2.3, 3.2]\n",
            "\n",
            "```\n",
            "\n",
            "In this example, we create a new Chroma client and reset the client to make sure it's empty. \n",
            "\n",
            "Next, we create a collection named \"test_collection\". \n",
            "\n",
            "We then add two documents with their corresponding embeddings to the collection. \n",
            "\n",
            "Finally, we get the embeddings of a document with id \"doc1\" using the `collection.get()` method with the `include` parameter set to `[chromadb.GetEmbeddingIncludeEnum.Embeddings]`.\n",
            "\n",
            "I hope this helps! Let me know if you need more information or have any other questions.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ask('What is the Python class for Chroma query interface?', chat_history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23vpHyrY-sMv",
        "outputId": "dafa707e-8857-4487-e6bc-9fac4ae80bd6"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question:\n",
            " What is the Python class for Chroma query interface?\n",
            "\n",
            "Answer:\n",
            " The Python class for the Chroma query interface is `chromadb.Client`.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ask('Show me the public functions of class Client', chat_history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jw2V0-pa_AAp",
        "outputId": "ad403141-77d2-4c7c-9383-e0f373e1045f"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question:\n",
            " Show me the public functions of class Client\n",
            "\n",
            "Answer:\n",
            " The `Client` class in Chroma has the following public functions: \n",
            "- `createCollection`: creates a collection with a given name and metadata\n",
            "- `getCollection`: returns a collection with a given name\n",
            "- `getOrCreateCollection`: returns a collection with a given name, creating it if it does not exist\n",
            "- `deleteCollection`: deletes a collection with a given name\n",
            "- `listCollections`: returns a list of all collections in the Chroma instance\n",
            "- `reset`: resets the Chroma instance, deleting all collections and reseting all indexes\n",
            "- `version`: returns the version of the Chroma instance\n",
            "- `heartbeat`: returns the time since the last request to the Chroma instance.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ask('What are the underlying databases used by Chroma?', chat_history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cDG3CTK5AftH",
        "outputId": "15e696d2-86bf-434a-be50-1dc4eff5899e"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question:\n",
            " What are the underlying databases used by Chroma?\n",
            "\n",
            "Answer:\n",
            " Chroma provides a JS/TS interface to talk to a backend Chroma DB over REST. It uses different backend implementations for the database. By default, Chroma uses the duckdb+parquet backend implementation, but other implementations such as SQLite, MySQL, and PostgreSQL are possible. You can also use Chroma with an in-memory backend for easy prototyping, and persist to disk when you're ready.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ask('Which class implements the DuckDB support?', chat_history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "reZAo8xPAvQg",
        "outputId": "d1f1ebaf-52fb-4274-93ee-ffd131d41052"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question:\n",
            " Which class implements the DuckDB support?\n",
            "\n",
            "Answer:\n",
            " The `DuckDB` support in Chroma is implemented by the `DuckDB` class in the `chromadb.db.duckdb` module.\n",
            "\n"
          ]
        }
      ]
    }
  ]
}